收稿日期: 2008-12-30
修回日期: 2009-06-11
网络出版日期: 2009-07-10
基金资助
国家重点基础研究发展计划项目“陆表生态环境要素主被动协同反演理论与方法”（编号：2007CB714404）;中国科学院西部行动计划（二期）项目“黑河流域遥感—地面观测同步试验与综合模拟平台建设”（编号：KZCX2-XB2-09）资助.
A Study of Forest Parameters Mapping Technique Using Airborne LIDAR Data
Received date: 2008-12-30
Revised date: 2009-06-11
Online published: 2009-07-10
森林结构参数诸如林分平均高、平均冠幅、平均胸径、林分密度、地上生物量等的空间分布对于森林可持续经营管理具有重要意义。以黑河流域祁连山大野口典型森林区为研究区，采用高密度LIDAR小脚印点云数据，在进行单木结构参数提取的基础上，按20 m×20 m大小的网格进行了小区域森林参数反演研究。首先由LIDAR点云数据生成冠层高度模型（Canopy Height Model，CHM），从CHM中估测单株木结构参数树的位置、树高、冠幅。然后采用多元逐步回归分析法建立样地（20 m×20 m）尺度上LIDAR估测的平均树高、冠幅等与实测森林参数（林分平均高、林分算数平均高、平均冠幅、平均胸径、林分密度、地上生物量）之间的关系。结果表明，林分平均高、林分算术平均高、地上生物量的估测方程精度较高，R2均大于0.7，平均冠幅、平均胸径、林分密度的估测方程R2均大于0.5，根据建立的方程得到了森林参数的空间分布图。高密度LIDAR数据可以得到较高精度的森林参数空间分布图，对于森林可持续经营管理以及林相图的更新等具有重要意义，同时对小流域森林水文科学的研究具有重要的应用价值。
刘清旺 , 庞勇 , 陈尔学 , 曹春香 , 何祺胜 . 基于LIDAR数据的森林参数反演方法研究[J]. 地球科学进展, 2009 , 24(7) : 748 -755 . DOI: 10.11867/j.issn.1001-8166.2009.07.0748
Estimating spatial forest stand variables such as mean height, mean crown diameter, mean diameter breast height DBH, tree density and aboveground biomass is important for sustainable forest management. This study aimed to estimate forest stand variables in coniferous tree species of Picea crassifolia stand in the Qilian Mountain, western China from single tree detection using small-footprint airborne LIDAR data. Based on the LIDAR data, a canopy height model (CHM) was firstly computed as the difference between tree canopy hits and the LIDAR terrain elevation values. In this study, a double-tangents crowns recognition algorithm was used to extract single tree location, height and crown polygon.
Stepwise multiple regression models were used to develop equations relating LIDAR-derived parameters, such as tree height, stand density and crown width, with observed forest parameters for each sample plot. The precision of equation for estimating mean stand height, tree density and aboveground biomass is high, with R2 bigger than 0.7. These results showed that the LIDAR data was useful for forest stand variables. Finally, the spatial forest stand variables maps were established using the stepwise multiple regression equations. The results showed that highdensity LIDAR data could be used to get forest variables distribution maps with relatively high precision, which was of important practical significance for sustainable forest management and update of forest form map, and for forest hydrological science research in small basin.
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